141 citations · 314 across the 16 of their papers we have counts for
20 papers
Conditional anomaly detection methods for patient-management alert systems
Michal Valko, Gregory Cooper, Amy Seybert +3
Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly det…
Outlier detection for patient monitoring and alerting
Miloš Hauskrecht, Iyad Batal, Michal Valko +3
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). O…
Conditional outlier detection for clinical alerting
Milos Hauskrecht, Michal Valko, Shyam Visweswaran +3
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) sys…
Evidence-based anomaly detection in clinical domains
Milos Hauskrecht, Michal Valko, Branislav Kveton +2
Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that…
Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines
David Oniani, Xizhi Wu, Shyam Visweswaran +4
Background Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, methods for incorpor…
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
Yanwu Xu, Li Sun, Wei Peng +7
This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly…
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
Sonish Sivarajkumar, Mark Kelley, Alyssa Samolyk-Mazzanti +2
Large language models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), especially in domains where labeled data is scarce or expensive, such as clini…
ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research
David Oniani, Bambang Parmanto, Andi Saptono +9
Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately…
Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study
Sonish Sivarajkumar, Fengyi Gao, Parker E. Denny +4
Post-stroke patient rehabilitation requires precise, personalized treatment plans. Natural Language Processing (NLP) offers potential to extract valuable exercise information from…
Hyperbolic Molecular Representation Learning for Drug Repositioning
Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich
Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations amo…
Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space
Ke Yu, Shyam Visweswaran, Kayhan Batmanghelich
Learning accurate drug representation is essential for tasks such as computational drug repositioning and prediction of drug side-effects. A drug hierarchy is a valuable source tha…
Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
Eric V. Strobl, Shyam Visweswaran
Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large. Most investigators estimate such perso…
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before…
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
Eric V. Strobl, Kun Zhang, Shyam Visweswaran
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one…
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values
Eric V. Strobl, Peter L. Spirtes, Shyam Visweswaran
The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimat…
Markov Boundary Discovery with Ridge Regularized Linear Models
Eric V. Strobl, Shyam Visweswaran
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to di…
Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data
Eric V. Strobl, Shyam Visweswaran
Motivation: Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods di…
Counting Markov Blanket Structures
Shyam Visweswaran, Gregory F. Cooper
Learning Markov blanket (MB) structures has proven useful in performing feature selection, learning Bayesian networks (BNs), and discovering causal relationships. We present a form…
Markov Blanket Ranking using Kernel-based Conditional Dependence Measures
Eric V. Strobl, Shyam Visweswaran
Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algor…
Deep Multiple Kernel Learning
Eric Strobl, Shyam Visweswaran
Deep learning methods have predominantly been applied to large artificial neural networks. Despite their state-of-the-art performance, these large networks typically do not general…